We propose a new measure (phase-slope index) to estimate the direction of information flux in multivariate time series. This measure (a) is insensitive to mixtures of independent sources, (b) gives meaningful results even if the phase spectrum is not linear, and (c) properly weights contributions from different frequencies. These properties are shown in extended simulations and contrasted to Granger causality which yields highly significant false detections for mixtures of independent sources. An application to electroencephalography data (eyes-closed condition) reveals a clear front-to-back information flow.

Social networking sites like MySpace, Facebook, and StudiVZ are popular means of communicating personality. Recent theoretical and empirical considerations of homepages and Web 2.0 platforms show that impression management is a major motive for actively participating in social networking sites. However, the factors that determine the specific form of self-presentation and the extent of self-disclosure on the Internet have not been analyzed. In an exploratory study, we investigated the relationship between self-reported (offline) personality traits and (online) self-presentation in social networking profiles. A survey among 58 users of the German Web 2.0 site, StudiVZ.net, and a content analysis of the respondents’ profiles showed that self-efficacy with regard to impression management is strongly related to the number of virtual friends, the level of profile detail, and the style of the personal photo. The results also indicate a slight influence of extraversion, whereas there was no significant effect for self-esteem.

Graphical Gaussian models are popular tools for the estimation of (undirected) gene association networks from microarray data. A key issue when the number of variables greatly exceeds the number of samples is the estimation of the matrix of partial correlations. Since the (Moore-Penrose) inverse of the sample covariance matrix leads to poor estimates in this scenario, standard methods are inappropriate and adequate regularization techniques are needed. Popular approaches include biased estimates of the covariance matrix and high-dimensional regression schemes, such as the Lasso and Partial Least Squares.

Trust has been recognized as a central variable to explain the resistance to using automated systems (under-trust) and the overreliance on automated systems (over-trust). To achieve appropriate reliance, users’ trust should be calibrated to reflect a system’s capabilities. Studies from various disciplines have examined different interventions to attain such trust calibration. Based on a literature body of 1000+ papers, we identified 96 relevant publications which aimed to calibrate users’ trust in automated systems. To provide an in-depth overview of the state-of-the-art, we reviewed and summarized measurements of the trust calibration, interventions, and results of these efforts. For the numerous promising calibration interventions, we extract common design choices and structure these into four dimensions of trust calibration interventions to guide future studies. Our findings indicate that the measurement of the trust calibration often limits the interpretation of the effects of different interventions. We suggest future directions for this problem.